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Model Selection

Given a set of candidate models, the goal of Model Selection is to select the model that best approximates the observed data and captures its underlying regularities. Model Selection criteria are defined such that they strike a balance between the goodness of fit, and the generalizability or complexity of the models.

Source: Kernel-based Information Criterion

Papers

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Showing 511–520 of 2050 papers

TitleStatusHype
Selective machine learning of doubly robust functionals—0
Local Projections Inference with High-Dimensional Covariates without Sparsity—0
Deriving Emotions and Sentiments from Visual Content: A Disaster Analysis Use Case—0
Beyond similarity assessment: Selecting the optimal model for sequence alignment via the Factorized Asymptotic Bayesian algorithm—0
A Novel Approach to Eliminating Hallucinations in Large Language Model-Assisted Causal Discovery—0
A Comprehensive Evaluation of Large Language Models on Mental Illnesses in Arabic Context—0
Beyond One-Size-Fits-All: Multi-Domain, Multi-Task Framework for Embedding Model Selection—0
Beyond Glucose-Only Assessment: Advancing Nocturnal Hypoglycemia Prediction in Children with Type 1 Diabetes—0
A Dirichlet stochastic block model for composition-weighted networks—0
A Bandit Approach with Evolutionary Operators for Model Selection—0
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